Tooraj Sadeghi and Kambiz Heidarzadeh Hanzaee
This paper seeks to investigate the key factors underlying customer satisfaction with electronic banking services in an Islamic country, Iran.
Abstract
Purpose
This paper seeks to investigate the key factors underlying customer satisfaction with electronic banking services in an Islamic country, Iran.
Design/methodology/approach
The authors validate a measurement model for customer satisfaction evaluation in e‐banking service quality based on different service quality models and theories such as technology acceptance model, theory of reasoned action and theory of planned behavior.
Findings
The paper provides a model of seven factors on the following dimensions: convenience, accessibility, accuracy, security, usefulness, bank image, and web site design. Some of these factors illustrate a significant statistical difference between males and females.
Originality/value
These dimensions are determinants of customer's quality perception in e‐banking services and this paper presents new directions in service quality research and offers new directions to researchers and managers in providing service quality improvement.
Details
Keywords
Tooraj Karimi, Mohammad Reza Sadeghi Moghadam and Amirhosein Mardani
This paper aims to design an expert system that gets data from researchers and determines their maturity level. This system can be used for determining researchers’ support…
Abstract
Purpose
This paper aims to design an expert system that gets data from researchers and determines their maturity level. This system can be used for determining researchers’ support programs as well as a tool for researchers in research-based organizations.
Design/methodology/approach
This study focuses on designing the inference engine as a component of an expert system. To do so, rough set theory is used to design rule models. Various complete, discretizing and reduction algorithms are used in this paper, and different models were run.
Findings
The proposed inference engine has the validity of 99.8 per cent, and the most important attributes to determine the maturity level of researchers in this model are “commitment to research” and “attention to research plan timeline”.
Research limitations/implications
To accurately determine researchers’ maturity model, solely referring to documents and self-reports may reduce the validation. More validation could be reached through using assessment centers for determining capabilities of samples and observations in each maturity level.
Originality/value
The assessment system for the professional maturity of researchers is an appropriate tool for funders to support researchers. This system helps the funders to rank, validate and direct researchers. Furthermore, it is a valid criterion for researchers to evaluate and improve their abilities. There is not any expert system to assess the researches in literature, and all models, frameworks and software are conceptual or self-assessment.
Details
Keywords
Tooraj Karimi and Arvin Hojati
In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used…
Abstract
Purpose
In this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.
Design/methodology/approach
Some tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.
Findings
In this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.
Research limitations/implications
The total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.
Originality/value
The main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.